基于神经网络的车辆识别号码识别

Q3 Engineering
Meng Fanjun, Yin Dong
{"title":"基于神经网络的车辆识别号码识别","authors":"Meng Fanjun, Yin Dong","doi":"10.12086/OEE.2021.200094","DOIUrl":null,"url":null,"abstract":"It is far essential to properly recognize the vehicle identification number (VIN) engraved on the car frame for vehicle surveillance and identification. In this paper, we propose an algorithm for recognizing rotational VIN im-ages based on neural network which incorporates two components: VIN detection and VIN recognition. Firstly, with lightweight neural network and text segmentation based on EAST, we attain efficient and excellent VIN detection performance. Secondly, the VIN recognition is regarded as a sequence classification problem. By means of connecting sequential classifiers, we predict VIN characters directly and precisely. For validating our algorithm, we collect a VIN dataset, which contains raw rotational VIN images and horizontal VIN images. Experimental results show that the algorithm we proposed achieves good performance on VIN detection and VIN recognition in real time.","PeriodicalId":39552,"journal":{"name":"光电工程","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vehicle identification number recognition based on neural network\",\"authors\":\"Meng Fanjun, Yin Dong\",\"doi\":\"10.12086/OEE.2021.200094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is far essential to properly recognize the vehicle identification number (VIN) engraved on the car frame for vehicle surveillance and identification. In this paper, we propose an algorithm for recognizing rotational VIN im-ages based on neural network which incorporates two components: VIN detection and VIN recognition. Firstly, with lightweight neural network and text segmentation based on EAST, we attain efficient and excellent VIN detection performance. Secondly, the VIN recognition is regarded as a sequence classification problem. By means of connecting sequential classifiers, we predict VIN characters directly and precisely. For validating our algorithm, we collect a VIN dataset, which contains raw rotational VIN images and horizontal VIN images. Experimental results show that the algorithm we proposed achieves good performance on VIN detection and VIN recognition in real time.\",\"PeriodicalId\":39552,\"journal\":{\"name\":\"光电工程\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"光电工程\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.12086/OEE.2021.200094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"光电工程","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.12086/OEE.2021.200094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

正确识别镌刻在车架上的车辆识别号码对于车辆监控和识别至关重要。本文提出了一种基于神经网络的旋转VIN图像识别算法,该算法包含两个部分:VIN检测和VIN识别。首先,利用轻量级神经网络和基于EAST的文本分割,实现了高效、优异的识别码检测性能;其次,将VIN识别视为一个序列分类问题。通过连接顺序分类器,可以直接准确地预测VIN字符。为了验证我们的算法,我们收集了一个VIN数据集,其中包含原始的旋转VIN图像和水平VIN图像。实验结果表明,该算法在VIN检测和实时识别方面取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle identification number recognition based on neural network
It is far essential to properly recognize the vehicle identification number (VIN) engraved on the car frame for vehicle surveillance and identification. In this paper, we propose an algorithm for recognizing rotational VIN im-ages based on neural network which incorporates two components: VIN detection and VIN recognition. Firstly, with lightweight neural network and text segmentation based on EAST, we attain efficient and excellent VIN detection performance. Secondly, the VIN recognition is regarded as a sequence classification problem. By means of connecting sequential classifiers, we predict VIN characters directly and precisely. For validating our algorithm, we collect a VIN dataset, which contains raw rotational VIN images and horizontal VIN images. Experimental results show that the algorithm we proposed achieves good performance on VIN detection and VIN recognition in real time.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
光电工程
光电工程 Engineering-Electrical and Electronic Engineering
CiteScore
2.00
自引率
0.00%
发文量
6622
期刊介绍:
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信